This chapter covers two frequently used algorithms for motif characterization and prediction. The first part is on the position weight matrix (PWM) algorithm, which takes in a set of aligned motif sequences (e.g., 5′ splice sites), characterizes the motif by site-specific patterns, and generates a list of PWM scores, which may be taken as the signal strength for each input sequence. PWM for sequences is equivalent to a principal component analysis that reduces a multidimensional matrix to a single dimension. Also generated are the significance tests of the site-specific motif patterns. PWM also serves as an essential component in algorithms for de novo motif discovery, such as the Gibbs sampler. How to specify background frequencies and pseudo-counts in computing PWM? What are their effects on the PWM outcome? How to control for the type I error rate involving multiple comparisons by using the false discovery rate? All these topics are detailed in this chapter. The second part of the chapter covers the perceptron algorithm, which is the simplest artificial neural network with only a single neuron. Its function is equivalent to two-group discriminant function analysis in multivariate statistics, i.e., to identify nucleotide or amino acid sites that provide the greatest discriminant power between two sets of sequences. The perceptron algorithm, as well as its application and limitations, are illustrated in detail, together with new approaches to circumvent the XOR problem inherent in the perceptron algorithm.